
import numpy as np
import librosa
import librosa.display as ld
import matplotlib.pyplot as plt
from IPython.display import Audio
import pywt
import emd


# importando o  áudio
audio_file = 'audio1.mp3'
data, sample_rate = librosa.load(audio_file,sr=None)
# vetor em função do tempo
t = librosa.times_like(data)

# gerando plot do audio
plt.figure(figsize=(12, 2))
plt.plot(t, data, linewidth=0.5)
plt.title('Sinal de Áudio')
plt.xlabel('Tempo (s)')
plt.ylabel('Amplitude')
plt.xlim([t.min(), t.max()])
plt.show()

# exibindo diplay do áudio
Audio(data, rate=sample_rate)


# Parâmetros da STFT
n_fft = int(sample_rate * 0.03)  # Tamanho da janela(30 ms)
hop_length = int(sample_rate * 0.01)  # Intervalo de 10ms entre janelas
window = 'hann' 

# Realizar a STFT
D = librosa.stft(data, n_fft=n_fft, hop_length=hop_length, window=window)

# Gerar spectrograma em dB
DB = librosa.amplitude_to_db(abs(D), ref=np.max)

# Plotando o espectrograma
plt.figure(figsize=(10, 6))
librosa.display.specshow(DB, sr=sample_rate, hop_length=hop_length, x_axis='time', y_axis='linear')
plt.colorbar(format='%+2.0f dB')
plt.title('Espectrograma')
plt.show()


# Array com tamanhos de janela em milissegundos variando de 10 a 100 ms
window_sizes_ms = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]

plt.figure(figsize=(15, 10))

for i, window_size_ms in enumerate(window_sizes_ms):
    n_fft = int(sample_rate * (window_size_ms / 1000))
    hop_length = int(n_fft * 0.2)

    # STFT
    D = librosa.stft(data, n_fft=n_fft, hop_length=hop_length, window='hann')

    # Espectrograma em dB
    DB = librosa.amplitude_to_db(abs(D), ref=np.max)

    # Plotando o espectrograma
    plt.subplot(5, 2, i+1)
    librosa.display.specshow(DB, sr=sample_rate, hop_length=hop_length, x_axis='time', y_axis='linear')
    plt.colorbar(format='%+2.0f dB')
    plt.title(f'Janela {window_size_ms} ms')

plt.tight_layout()
plt.show()


# Decomposição DWT com 8 níveis usando a wavelet Daubechies 4
wavelet = 'db4'
levels = 8
coeffs = pywt.wavedec(data, wavelet, level=levels)

# Aproximação em cada nível
approximations = [coeffs[0]]
details = coeffs[1:]

# Gerando figura para plot dos resultados
plt.figure(figsize=(14, 18))

# Plotando o sinal original
plt.subplot(levels + 2, 1, 1)
plt.plot(data)
plt.title('Sinal Original')

# Plotando cada nível
for i, detail in enumerate(details):
    plt.subplot(levels + 2, 1, i + 2)
    plt.plot(detail)
    plt.title(f'Detalhes Nível {i+1}')

plt.subplot(levels + 2, 1, levels + 2)
plt.plot(approximations[-1])
plt.title(f'Aproximação Nível {levels}')

plt.tight_layout()
plt.show()


# Array de alguns wavelets-mãe
wavelets = ['db1', 'db2', 'db3']

# Loop das wavelets-mãe definidas acima
for i, wavelet_name in enumerate(wavelets):
    # Decomposição DWT com 8 níveis usando a wavelet atual
    coeffs = pywt.wavedec(data, wavelet_name, level=8)

    # Recuperando os detalhes e a aproximação em cada nível
    approximations = [coeffs[0]]
    details = coeffs[1:]

    # Criando uma figura para cada wavelet-mãe
    plt.figure(figsize=(14, 3 * len(details)))

    # Plotando original
    plt.subplot(len(details) + 1, 1, 1)
    plt.plot(data)
    plt.title('Sinal Original')

    # Plotando o nível em linhas distintas
    for j, detail in enumerate(details):
        plt.subplot(len(details) + 1, 1, j + 2)
        plt.plot(detail)
        plt.title(f'Detalhe Nível {j+1}')

    # Ajustando os espaços entre os subplots
    plt.tight_layout()
    plt.suptitle(f'Wavelet-Mãe: {wavelet_name}', y=1.05)
    plt.show()


peak_freq = 15
seconds = 20
noise_std = .4
x = emd.simulate.ar_oscillator(peak_freq, sample_rate, seconds,
                               noise_std=noise_std, random_seed=42, r=.96)[:, 0]
x = x*1e-4
t = np.linspace(0, seconds, seconds*sample_rate)

# sphinx_gallery_thumbnail_number = 6


# Plot the first 5 seconds of data
plt.figure(figsize=(12, 2))
plt.plot(t[:sample_rate*3], x[:sample_rate*3], 'k')


imf = emd.sift.mask_sift(x, max_imfs=10)

emd.plotting.plot_imfs(imf[:sample_rate*3, :])

<Axes: xlabel='Time (samples)'>


IP, IF, IA = emd.spectra.frequency_transform(imf, sample_rate, 'nht')


freq_edges, freq_centres = emd.spectra.define_hist_bins(1, 5, 4)
print('Bin Edges:   {0}'.format(freq_edges))
print('Bin Centres: {0}'.format(freq_centres))
freq_edges, freq_centres = emd.spectra.define_hist_bins(1, 50, 8, 'log')

# We round the values to 3dp for easier visualisation
print('Bin Edges:   {0}'.format(np.round(freq_edges, 3)))
print('Bin Centres: {0}'.format(np.round(freq_centres, 3)))

freq_edges, freq_centres = emd.spectra.define_hist_bins(1, 50, 8, 'log')
f, spectrum = emd.spectra.hilberthuang(IF, IA, freq_edges)

f, spectrum = emd.spectra.hilberthuang(IF, IA, (1, 50, 25))

freq_edges, freq_centres = emd.spectra.define_hist_bins(0, 100, 128, 'linear')

# Amplitude weighted HHT per IMF
f, spec_weighted = emd.spectra.hilberthuang(IF, IA, freq_edges, sum_imfs=False)

# Unweighted HHT per IMF - we replace the instantaneous amplitude values with ones
f, spec_unweighted = emd.spectra.hilberthuang(IF, np.ones_like(IA), freq_edges, sum_imfs=False)

Bin Edges:   [1. 2. 3. 4. 5.]
Bin Centres: [1.5 2.5 3.5 4.5]
Bin Edges:   [ 1.     1.631  2.659  4.336  7.071 11.531 18.803 30.662 50.   ]
Bin Centres: [ 1.315  2.145  3.498  5.704  9.301 15.167 24.732 40.331]


plt.figure(figsize=(10, 4))
plt.subplots_adjust(hspace=0.4)
plt.subplot(121)
plt.plot(freq_centres, spec_unweighted)
plt.xticks(np.arange(10)*10)
plt.xlim(0, 100)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Count')
plt.title('unweighted\nHilbert-Huang Transform')

plt.subplot(122)
plt.plot(freq_centres, spec_weighted)
plt.xticks(np.arange(10)*10)
plt.xlim(0, 100)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Power')
plt.title('IA-weighted\nHilbert-Huang Transform')
plt.legend(['IMF-1', 'IMF-2', 'IMF-3', 'IMF-4', 'IMF-5'], frameon=False)

<matplotlib.legend.Legend at 0x209aac090f0>


import numpy as np
from scipy.io import wavfile
import matplotlib.pyplot as plt
from pyemd import CEEMDAN

# Carregar o arquivo de áudio
audio_file = 'audio.wav'  
sample_rate, audio_data = wavfile.read(audio_file)

# Certifique-se de que o áudio seja mono (um único canal)
if len(audio_data.shape) > 1:
    audio_data = audio_data[:, 0]

# Normalizar o áudio para valores entre -1 e 1
audio_data = audio_data.astype(np.float32)
audio_data /= np.max(np.abs(audio_data))

# Criar um vetor de tempo para o áudio
time = np.arange(len(audio_data)) / sample_rate

# Aplicar a CEEMDAN ao áudio
ceemdan = CEEMDAN()
ceemdan_results = ceemdan(audio_data, max_imf=-1)

# Plotar as IMFs resultantes da CEEMDAN
num_imfs = len(ceemdan_results)
plt.figure(figsize=(12, 8))
for i, imf in enumerate(ceemdan_results):
    plt.subplot(num_imfs, 1, i + 1)
    plt.plot(time, imf)
    plt.title(f'CEEMDAN IMF {i + 1}')

plt.tight_layout()
plt.show()

---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
c:\Users\claud\Desktop\teste\teste.ipynb Cell 24 line 4
      <a href='vscode-notebook-cell:/c%3A/Users/claud/Desktop/teste/teste.ipynb#X34sZmlsZQ%3D%3D?line=1'>2</a> from scipy.io import wavfile
      <a href='vscode-notebook-cell:/c%3A/Users/claud/Desktop/teste/teste.ipynb#X34sZmlsZQ%3D%3D?line=2'>3</a> import matplotlib.pyplot as plt
----> <a href='vscode-notebook-cell:/c%3A/Users/claud/Desktop/teste/teste.ipynb#X34sZmlsZQ%3D%3D?line=3'>4</a> from pyemd import EEMD
      <a href='vscode-notebook-cell:/c%3A/Users/claud/Desktop/teste/teste.ipynb#X34sZmlsZQ%3D%3D?line=5'>6</a> # Carregar o arquivo de áudio
      <a href='vscode-notebook-cell:/c%3A/Users/claud/Desktop/teste/teste.ipynb#X34sZmlsZQ%3D%3D?line=6'>7</a> audio_file = 'audio.wav'

File c:\Users\claud\AppData\Local\Programs\Python\Python310\lib\site-packages\pyemd\__init__.py:6
      3 __version__ = "1.5.1"
      4 logger = logging.getLogger("pyemd")
----> 6 from PyEMD.CEEMDAN import CEEMDAN  # noqa
      7 from PyEMD.EEMD import EEMD  # noqa
      8 from PyEMD.EMD import EMD  # noqa

ModuleNotFoundError: No module named 'PyEMD'